sklearn.grid_search.RandomizedSearchCV

class sklearn.grid_search.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None)

Randomized search on hyper parameters.

RandomizedSearchCV implements a “fit” method and a “predict” method like any classifier except that the parameters of the classifier used to predict is optimized by cross-validation.

In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.

Parameters:

estimator : object type that implements the “fit” and “predict” methods

A object of that type is instantiated for each parameter setting.

param_distributions : dict

Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly.

n_iter : int, default=10

Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.

scoring : string, callable or None, optional, default: None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

fit_params : dict, optional

Parameters to pass to the fit method.

n_jobs : int, optional

Number of jobs to run in parallel (default 1).

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

• None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
• An int, giving the exact number of total jobs that are spawned
• A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’

iid : boolean, optional

If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

cv : integer or cross-validation generator, optional

If an integer is passed, it is the number of folds (default 3). Specific cross-validation objects can be passed, see sklearn.cross_validation module for the list of possible objects

refit : boolean

Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.

verbose : integer

Controls the verbosity: the higher, the more messages.

Attributes:

`grid_scores_` : list of named tuples

Contains scores for all parameter combinations in param_grid. Each entry corresponds to one parameter setting. Each named tuple has the attributes:

• parameters, a dict of parameter settings
• mean_validation_score, the mean score over the cross-validation folds
• cv_validation_scores, the list of scores for each fold

`best_estimator_` : estimator

Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data.

`best_score_` : float

Score of best_estimator on the left out data.

`best_params_` : dict

Parameter setting that gave the best results on the hold out data.

See also

GridSearchCV

Does exhaustive search over a grid of parameters.

ParameterSampler

A generator over parameter settins, constructed from param_distributions.

Notes

The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.

If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

Methods

fit(X[, y])	Run fit on the estimator with randomly drawn parameters.
get_params([deep])	Get parameters for this estimator.
score(X[, y])	Returns the score on the given test data and labels, if the search estimator has been refit.
set_params(**params)	Set the parameters of this estimator.

__init__(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None)

fit(X, y=None)

Run fit on the estimator with randomly drawn parameters.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

score(X, y=None)

Returns the score on the given test data and labels, if the search estimator has been refit. The score function of the best estimator is used, or the scoring parameter where unavailable.

Parameters:

X : array-like, shape = [n_samples, n_features]

Input data, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

Returns:

score : float

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :